Search Results (143)

The impact mechanism of long-term creep in gas-containing coal on coal and gas outbursts has not been fully elucidated and remains insufficiently understood for the purpose of disaster engineering control. This investigation conducted triaxial creep experiments on raw coal specimens under controlled confining pressures, axial stresses, and gas pressures. Through systematic analysis of coal’s physical responses across different loading conditions, we developed and validated a novel creep damage constitutive model for gas-saturated coal through laboratory data calibration. The key findings reveal three characteristic creep regimes: (1) a decelerating phase dominates under low stress conditions, (2) progressive transitions to combined decelerating–steady-state creep with increasing stress, and (3) triphasic decelerating–steady–accelerating behavior at critical stress levels. Comparative analysis shows that gas-free specimens exhibit lower cumulative strain than the 0.5 MPa gas-saturated counterparts, with gas presence accelerating creep progression and reducing the time to failure. Measured creep rates demonstrate stress-dependent behavior: primary creep progresses at 0.002–0.011%/min, decaying exponentially to secondary creep rates below 0.001%/min. Steady-state creep rates follow a power law relationship when subject to deviatoric stress (R² = 0.96). Through the integration of Burgers viscoelastic model with the effective stress principle for porous media, we propose an enhanced constitutive model, incorporating gas adsorption-induced dilatational stresses. This advancement provides a theoretical foundation for predicting time-dependent deformation in deep coal reservoirs and informs monitoring strategies concerning gas-bearing strata stability. This study contributes to the theoretical understanding and engineering monitoring of creep behavior in deep coal rocks. Full article

Rowing is a cyclic sport that consists of repetitive biomechanical actions, with performance being influenced by the balance between propulsive and resistive forces. The current study aimed to assess the relationships between intracycle velocity variation (IVV) and key biomechanical and performance variables in male and female single scullers. Twenty-three experienced rowers (10 females) completed a 2000 m rowing competition, during which boat position and velocity were measured using a 15 Hz GPS, while cycle rate was derived from the integrated triaxial accelerometer sampling at 100 Hz. From these data, it was possible to calculate distance per cycle, IVV, the coefficient of velocity variation (CVV), and technical index values. Males presented higher mean, maximum and minimum velocity, distance per cycle, CVV, and technical index values than females (15.40 ± 0.81 vs. 13.36 ± 0.88 km/h, d = 0.84; 21.39 ± 1.68 vs. 18.77 ± 1.52 km/h, d = 1.61; 11.15 ± 1.81 vs. 9.03 ± 0.85 km/h, d = 1.45; 7.68 ± 0.32 vs. 6.89 ± 0.97 m, d = 0.69; 14.13 ± 2.02 vs. 11.64 ± 1.93%, d = 2.06; and 34.25 ± 4.82 vs. 26.30 ± 4.23 (m²/s·cycle), d = 4.56, respectively). An association between mean velocity and intracycle IVV, CVV, and cycle rate (r = 0.68, 0.74 and 0.65, respectively) was observed in males but not in female single scullers (which may be attributed to anthropometric specificities). In female single scullers, mean velocity was related with distance per cycle and was associated with technical index in both males and females (r = 0.76 and 0.66, respectively). Despite these differences, male and female single scullers adopted similar pacing strategies and CVV remained constant throughout the 2000 m race (indicating that this variable might not be affected by fatigue). Differences were also observed in the velocity–time profile, with men reaching peak velocity first and having a faster propulsive phase. Data provided new information on how IVV and CVV relate to commonly used biomechanical variables in rowing. Technical index (r = 0.87): distance per cycle was associated with technical index in both males and females (r = 0.76 and 0.66, respectively). Future studies should include other boat classes and other performance variables such as the power output and arc length. Full article

To address the limitations of conventional single-track rail systems in challenging hilly and mountainous terrains, which are ill-suited for transporting heavy agricultural machinery, there is a critical need to develop a specialized the double-track rail transportation system optimized for orchard equipment. Recognizing this requirement, our research team designed and implemented a double-track rail transportation system. In this innovative system, the rail functions as the pivotal component, with its structural properties significantly impacting the machine’s overall stability and operational performance. In this study, resistance strain gauges were employed to analyze the stress–strain distribution of the track under a full load of 750 kg, a critical factor in the system’s design. To further investigate the structural performance of the double-track rail, the impact hammer method was utilized in conjunction with triaxial acceleration sensors to conduct experimental modal analysis (EMA) under actual support conditions. By integrating the Eigensystem Realization Algorithm (ERA), the first 20 natural modes and their corresponding parameters were successfully identified with high precision. A comparative analysis between finite element simulation results and experimental measurements was performed, revealing the double-track rail’s inherent vibration characteristics under constrained modal conditions versus actual boundary constraints. These valuable findings serve as a theoretical foundation for the dynamic optimization of rail structures and the mitigation of resonance issues. The advancement of hilly and mountainous rail transportation systems holds significant promise for enhancing productivity and transportation efficiency in agricultural operations. Full article

The structural integrity of cement concrete pavements, paramount for ensuring traffic safety and operational efficiency, faces mounting challenges from the escalating burden of heavy-duty vehicular traffic. Precise characterisation of pavement dynamic responses under such conditions proves indispensable for implementing effective structural health monitoring and early warning system deployment. This investigation examines the triaxial dynamic response characteristics of cement concrete pavement subjected to low-speed, heavy-duty vehicular excitations, employing data acquired through in situ field measurements. A monitoring system incorporating embedded triaxial MEMS accelerometers was developed to capture vibration signals directly within the pavement structure. Raw data underwent preprocessing utilising a smoothing wavelet transform technique to attenuate noise, followed by empirical mode decomposition (EMD) and short-time energy (STE) analysis to scrutinise the time–frequency and energetic properties of triaxial vibration signals. The findings demonstrate that heavy, slow-moving vehicles generate substantial triaxial vibrations, with the vertical (Z-axis) response exhibiting the greatest amplitude and encompassing higher dominant frequency components compared to the horizontal (X and Y) axes. EMD successfully decomposed the complex signals into discrete intrinsic mode functions (IMFs), identifying high-frequency components (IMF1–IMF3) associated with transient vehicular impacts, mid-frequency components (IMF4–IMF6) presumably linked to structural and vehicle dynamics, and low-frequency components (IMF7–IMF9) representing system trends or ambient noise. The STE analysis of the selected IMFs elucidated the transient nature of axle loading, revealing pronounced, localised energy peaks. These findings furnish a comprehensive understanding of the dynamic behaviour of cement concrete pavements under heavy vehicle loads and establish a robust methodological framework for pavement performance assessment and refined axle load identification. Full article

Monitoring data provide valuable information on embankment dam behavior but are typically not integrated into a classical probabilistic safety assessment. This paper introduces a Bayesian-inspired methodology to directly integrate actual dam monitoring records into a Monte Carlo probabilistic safety assessment using a finite element framework, without recalibrating the original input parameters ‘distributions. After the baseline (unweighted) set of simulations is generated, the method assigns a weight coefficient to each simulation outcome based on the likelihood of matching monitoring data, effectively updating the baseline probabilistic analysis results. Therefore, such “weighted” analysis produces an updated probability distribution of the dam’s factor of safety (FS) that reflects both prior uncertainty of model parameters and actual monitoring data. To illustrate the approach, a case study of a rockfill triaxial test specimen is analyzed: a baseline probabilistic analysis yields a mean FS ~1.7, whereas the weighted analysis incorporating monitoring data reduces the mean FS to ~1.5 and narrows the variability. The weighted analysis suggests less favorable conditions than the baseline projections. This methodology offers a transparent, computationally tractable route for embedding monitoring evidence into reliability calculations, producing more reflective safety estimates of actual dam behavior. Full article

The structural reliability of bottom-fixed offshore wind turbines is generally influenced by the dispersion of and variability in soil properties, which affect their ultimate capacity, serviceability, and both the short- and long-term fatigue. During an earthquake, the soil–pile system is subjected to intense cyclic loads that can lead to stiffness and strength degradation, typically captured through cyclic soil models. Calibration of soil parameter variability is fundamental for reliable structural assessments of wind turbine integrity. In this study, a method to generate randomness of the parameters affecting cyclic soil degradation models is proposed. Fatigue parameters are quantified through random cyclic undrained triaxial tests conducted using the Discrete Element Method. Deterministic simulations are first performed based on experimental results from the Liquefaction Experiments and Analysis Project for validation. Subsequently, variability in the initial particle size distribution functions is introduced to generate random soil samples, and triaxial simulations are repeated to quantify the dispersion of soil fatigue parameters. The proposed procedure is then applied through Monte Carlo simulations on the IEA 15-MW reference wind turbine, which is subjected to both short- and long-duration earthquakes. The results demonstrate the significant impact of soil degradation on the bending moment envelope, as well as the effect of soil uncertainty on tower fatigue, assessed using the damage equivalent load approach. Full article

Sleep posture detection is a potentially important component of sleep quality assessment and health monitoring. Accurate identification of sleep postures can offer valuable insights into an individual’s sleep patterns, comfort levels, and potential health risks. For example, improper sleep postures may lead to musculoskeletal issues, respiratory disturbances, and even worsen conditions like sleep apnea. Additionally, for long-term bedridden patients, continuous monitoring of sleep postures is essential to prevent pressure ulcers and other complications. Traditional methods for sleep posture detection have several limitations: wearable sensors can disrupt natural sleep and cause discomfort, camera-based systems raise privacy concerns and are sensitive to environmental conditions, and pressure-sensing mats are often complex and costly. To address these issues, we have developed a low-cost non-contact sleeping posture detection system. Our system features eight optimally distributed triaxial accelerometers, providing a comfortable and non-contact front-end data acquisition unit. For sleep posture classification, we employ an improved density peak clustering algorithm that incorporates the K-nearest neighbor mechanism. Additionally, we have constructed a Parallel Convolutional Spatiotemporal Network (PCSN) by integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) modules. Experimental results demonstrate that the PCSN can accurately distinguish six sleep postures: prone, supine, left log, left fetus, right log, and right fetus. The average accuracy is 98.42%, outperforming most state-of-the-art deep learning models. The PCSN achieves the highest scores across all metrics: 98.64% precision, 98.18% recall, and 98.10% F1 score. The proposed system shows considerable promise in various applications, including sleep studies and the prevention of diseases like pressure ulcers and sleep apnea. Full article

Elevator fault diagnosis is critical for ensuring operational safety and reliability in modern vertical transportation systems. Traditional approaches, which rely on time- and frequency-domain signal analysis, often struggle with the issues such as noise sensitivity, inadequate feature extraction, and limited adaptability to complex scenarios. To address these challenges, this paper proposes a Graph Attention Recurrent Network (GARN) which integrates graph-structured signal representation with spatiotemporal feature learning. The GARN employs a limited penetrable visibility graph to transform raw vibration signals into noise-robust graph topologies, preserving critical patterns while suppressing high-frequency noise through controlled edge penetration. An adaptive attention mechanism dynamically fuses triaxial features to prioritize the most relevant information for fault diagnosis. The GARN combines a graph convolutional network to extract spatial correlations and a gated recurrent unit to capture temporal fault progression, enabling holistic and accurate fault classification. Experimental results based on real-world elevator datasets demonstrate the superior performance of the GARN, showcasing its strong noise resistance, adaptability to complex fault conditions, and ability to provide reliable and timely fault diagnosis, making it a robust solution for modern elevator systems. Full article

Understanding the coupled evolution mechanisms of stress, fracture, and seepage fields in overburden strata is critical for preventing water inrush disasters during fully mechanized mining in deep coal seams, particularly under complex hydrogeological conditions. To address this challenge, this study integrates laboratory experiments with FLAC3D numerical simulations to systematically investigate the multi-field coupling behavior in the Luotuoshan coal mine. Three types of coal rock samples—raw coal/rock (bending subsidence zone), fractured coal/rock (fracture zone), and broken rock (caved zone)—were subjected to triaxial permeability tests under varying stress conditions. The experimental results quantitatively revealed distinct permeability evolution patterns: the fractured samples exhibited a 23–48 × higher initial permeability (28.03 mD for coal, 13.54 mD for rock) than the intact samples (0.50 mD for coal, 0.21 mD for rock), while the broken rock showed exponential permeability decay (120.32 mD to 23.72 mD) under compaction. A dynamic permeability updating algorithm was developed using FISH scripting language, embedding stress-dependent permeability models (R² > 0.99) into FLAC3D to enable real-time coupling of stress–fracture–seepage fields during face advancement simulations. The key findings demonstrate four distinct evolutionary stages of pore water pressure: (1) static equilibrium (0–100 m advance), (2) fracture expansion (120–200 m, 484% permeability surge), (3) seepage channel formation (200–300 m, 81.67 mD peak permeability), and (4) high-risk water inrush (300–400 m, 23.72 mD stabilized permeability). The simulated fracture zone height reached 55 m, directly connecting with the overlying sandstone aquifer (9 m thick, 1 MPa pressure), validating field-observed water inrush thresholds. This methodology provides a quantitative framework for predicting water-conducting fracture zone development and optimizing real-time water hazard prevention strategies in similar deep mining conditions. Full article

Hydraulic fracturing significantly impacts water production. This makes it crucial to determine whether its effects on formation water production are beneficial or detrimental in complex reservoir stimulations. This paper gives the influence that acts on pore structure variations and irreducible water transformation by hydraulic fracturing; by using NMR and Micro-CT, pore-throat reconfiguration in core samples induced fracturing. Two main pore variation types were identified from CT images. To analyze the gas–water flow mechanisms in pre-fracturing and post-fracturing reservoir conditions, we tested quantifying changes in irreducible water transforms into movable water saturation by using a triaxial in situ flow system, thereby elucidating the impact of the hydraulic fracture on irreducible water saturation. The experiments demonstrate that pore structures are significantly modified in terms of connectivity and diameter through hydraulic fracturing. During damage zone formation, 12.4–19.2% of small pores coalesce into larger pores through integration of isolated spaces. This variation enhances fluid mobility, transforms 1.38–11.61% of irreducible water, and decreases starting pressure gradients by 1 MPa/100 m to 0.1 MPa/100 m. Modified pore structure leads to the iso-permeability point shifting toward higher water saturation. The gas-phase relative permeability at irreducible water saturation is two times as high as that of the matrix sample. Fractured zones show a 20–23% conversion efficiency of irreducible to movable water. In addition, based on the results of experimental data, hydraulic fracturing increased water production by 3607 to 9163 m³. However, this effect is only maintained during the first 3 to 6 months post-fracture. These results quantify the transformation of irreducible water into movable water in hydraulic fracturing. This study provides key performance indicators for gas reservoir applications. Full article

Pipeline steel is highly susceptible to hydrogen embrittlement (HE) in hydrogen environments, which compromises its structural integrity and operational safety. Existing studies have primarily focused on the degradation trends of mechanical properties in hydrogen environments, but there remains a lack of quantitative failure prediction models. To investigate the failure behavior of X65 pipeline steel under hydrogen environments, this paper utilized notched round bar specimens with three different radii and smooth round bar specimens to examine the effects of pre-charging time, the coupled influence of stress triaxiality and hydrogen concentration, and the coupled influence of strain rate and hydrogen concentration on the HE sensitivity of X65 pipeline steel. Fracture surface morphologies were characterized using scanning electron microscopy (SEM), revealing that hydrogen-enhanced localized plasticity (HELP) dominates failure mechanisms at low hydrogen concentrations, while hydrogen-enhanced decohesion (HEDE) becomes dominant at high hydrogen concentrations. The results demonstrate that increasing stress triaxiality or decreasing strain rate significantly intensifies the HE sensitivity of X65 pipeline steel. Based on the experimental findings, failure prediction models for X65 pipeline steel were developed under the coupled effects of hydrogen concentration and stress triaxiality as well as hydrogen concentration and strain rate, providing theoretical support and mathematical models for the engineering application of X65 pipeline steel in hydrogen environments. Full article

To address the limitations in conventional granular morphology characterization where excessive emphasis has been placed on elongation index (EI) while neglecting flatness index (FI) and their coupled interactions, this study establishes an EI/FI co-regulated dual-parameter morphological characterization framework. Through integrated triaxial compression experiments and discrete element simulations, we systematically investigate multi-scale mechanical responses spanning macroscopic stress–strain behavior to microscopic force-chain evolution. The results show that (1) the regulation of pore structure by morphological parameters presents non-linear characteristics, and (2) the evolution of peak shear strength is predominantly governed by morphological anisotropy. (3) The parabolic relationship between the maximum dilatancy angle and the morphological parameters is shown. (4) The micro mechanical analysis reveals that EI/FI parameters have limited influence on the statistical distribution characteristics of the contact force chain, but have a significant regulatory effect on the anisotropic evolution of the force-chain network. Full article

This study employs rock triaxial acoustic emission laboratory tests to investigate the activity of microfractures in plagiogranite from the Yebatan hydropower station dam area. By integrating interdisciplinary theories—including spatiotemporal single-link groups, fractal theory, complex networks, and graph theory—we develop a complex network model of rock microfractures. Results demonstrate that the microfracture network, characterized by the average degree (<k>) and clustering coefficient (<c>), undergoes distinct evolutionary stages during rock deformation and failure. The complex network parameters <k> and <c> undergo abrupt increases and decreases. These changes serve as characteristic indicators of the transition from stable to unstable states in rock deformation and failure, providing new insights into predicting rock failure and instability. Full article

A novel deployable reflector antenna for small satellites has been designed, fabricated, and experimentally validated. The reflector utilizes a doubly curved flexible surface manufactured from a triaxially woven fabric-reinforced silicone (TWFS) composite. By leveraging high-strain composite materials, the design enables a highly compact stowed configuration while maintaining precise surface accuracy upon deployment. The deployment mechanism is proposed to accommodate a 0.6 m diameter parabolic reflector within a minimal stowed volume, optimizing space efficiency for satellite integration. To validate this concept, a prototype of the reflector antenna has been fabricated and demonstrated the feasibility and effectiveness of the proposed approach. Full article

The distribution of in situ stress fields in reservoirs is critical for the accurate exploration and efficient exploitation of hydrocarbon resources, especially in deep, fault-developed strata where tectonic activities significantly complicate stress field patterns. To clarify the perturbation effects of faults on in situ stress fields in deep reservoirs, this study combines dynamic–static parameter conversion models derived from laboratory experiments (acoustic emission Kaiser effect and triaxial compression tests) with a coupled “continuous matrix–discontinuous fault” numerical framework implemented in FLAC^3D6.0. Focusing on the BKQ Formation reservoir in the MH area, China, we developed a multivariate regression-based inversion model integrating gravitational and bidirectional tectonic stress fields, validated against field measurements with errors of −2.96% to 9.07%. The key findings of this study include the following: (1) fault slip induces stress reductions up to 22.3 MPa near fault zones, with perturbation ranges quantified via exponential decay functions (184.91–317.74 m); (2) the “continuous matrix–discontinuous fault” coupling method resolves limitations of traditional continuum models by simulating fault slip through interface contact elements; and (3) stress redistribution exhibits NW-SE gradients, aligning with regional tectonic compression. These results provide quantitative guidelines for optimizing hydrocarbon development boundaries and hydraulic fracturing designs in faulted reservoirs. Full article